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Chapter 5

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PsychologyCourse Code

PSYB01H3Professor

David NussbaumChapter

5This

**preview**shows pages 1-2. to view the full**6 pages of the document.**PSYB01 Chapter 5 – Sampling and Survey Research

Selecting Research Participants

- Sampling: the selection of individuals or other entities to represent a larger population

- Census: to study the entire population of interest

- Better to survey a limited number so there are more resources for follow-up

procedures

Sample Planning

- Define the Population

oStudents, disabled persons, elderly and adult samples report similar levels of

happiness

oWhen Dieners analyzed surveys of happiness in countries around the world, the

average level of happiness varied markedly (satisfaction with life domains

varies across cultures)

oCross-population generalizability: compare results obtained from samples of

different populations

- Define Sample Components

oElements: elementary units, individual member of population whose

characteristics are to be measured

oSampling frame: List of all elements in a population

oPopulation: Entire set of individuals or other entities to study

oRepresentative sample: “looks like” population from which it was selected (in

unrepresentative sample, some characteristics are overrepresented or

underrepresented)

oSample generalizability depends on the amount of sampling error (difference

between sample and population)

oEstimating Sampling Error

Inferential Statistics: tool for calculating sampling error

Sampling distributions for many statistics have a normal shape

Random Sampling Error: Variation owing purely to chance

Sample Statistic: value of a statistic such as a mean, computed from

sample data

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PSYB01 Chapter 5 – Sampling and Survey Research

Population Parameter: Value of a statistic computed using the data for

an entire population. Sample statistic is an estimate of a population

parameter

Sampling Methods

-Probability Sampling Methods: Probability of selection is known and is not zero

- Non-probability Sampling Methods: Sampling methods that do not let us know in

advance the likelihood of selecting each element

- Probability of Selection: likelihood that an element will be selected from the population

for inclusion in the sample

Probability Sampling Methods

- These methods have no systematic bias (nothing but chance determines which

elements are included in the sample)

- Four most common methods for drawing random samples

oSimple Random Sampling: procedure where cases are identified on basis of

chance

Random digit dialing: machine dials random numbers within the phone

prefixes

oSystematic Random Sampling: variant of simple random sampling

First element is selected randomly and then every nth element is

selected

Watch out for periodicity (sequence varies in some regular, periodic

pattern) (e.g. houses counted and the house on northwest side always

chosen because of sampling interval [number of cases from one

sampled case to another])

In that situation, starting point needs to be changed

oStratified Random Sampling: uses information known about population prior to

sampling to make sampling process more efficient

Ensures appropriate representation of elements across strata

Proportionate Stratified Sampling: ensures that sample is selected so

that the distribution of characteristics in the sample matches the

population

Disproportionate Stratified Sampling: Sampling where characteristics of

sample are disproportionate to population

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